Gene expression shapes the patterns of parallel evolution of herbicide resistance in the agricultural weed <i>Monochoria vaginalis</i>
Bibliographic record
Abstract
Abstract The evolution of herbicide resistance in weeds is an example of parallel evolution, through which genes encoding herbicide target proteins are repeatedly represented as evolutionary targets. The number of herbicide target-site genes differs among species, and little is known regarding the effects of duplicate gene copies on the evolution of herbicide resistance. We investigated the evolution of herbicide resistance in Monochoria vaginalis , which carries five copies of sulfonylurea target-site acetolactate synthase ( ALS ) genes. Suspected resistant populations collected across Japan were investigated for herbicide sensitivity and ALS gene sequences, followed by functional characterisation and ALS gene expression analysis. We identified over 60 resistant populations, all of which carried resistance-conferring amino acid substitutions exclusively in MvALS1 or MvALS3 . All MvALS4 alleles carried a loss-of-function mutation. Although the enzymatic properties of ALS encoded by these genes were not markedly different, the expression of MvALS1 and MvALS3 was prominently higher among all ALS genes. The higher expression of MvALS1 and MvALS3 is the driving force of the biased representation of genes during the evolution of herbicide resistance in M. vaginalis . Our findings highlight that gene expression is a key factor in creating evolutionary hotspots.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".